{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "13cb272e",
   "metadata": {
    "id": "13cb272e"
   },
   "source": [
    "# Code documentation Q&A bot example with LangChain\n",
    "![picture](https://lancedb.github.io/lancedb/assets/ecosystem-illustration.png)\n",
    "\n",
    "This Q&A bot will allow you to query your own documentation easily using questions. We'll also demonstrate the use of LangChain and LanceDB using the OpenAI API.\n",
    "\n",
    "In this example we'll **Numpy 1.26** documentation, but, this could be replaced for your own docs as well"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1991331f-4316-417a-b693-e2f27cbe9ea7",
   "metadata": {
    "id": "1991331f-4316-417a-b693-e2f27cbe9ea7"
   },
   "source": [
    "### Installing dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e8a49c31",
   "metadata": {
    "id": "e8a49c31",
    "outputId": "a9aca639-0c3d-4164-bf9c-7bdae7bc92f1",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Requirement already satisfied: langchain in /usr/local/lib/python3.10/dist-packages (0.3.9)\n",
      "Requirement already satisfied: langchain-openai in /usr/local/lib/python3.10/dist-packages (0.2.10)\n",
      "Collecting langchain-community\n",
      "  Downloading langchain_community-0.3.8-py3-none-any.whl.metadata (2.9 kB)\n",
      "Requirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.10/dist-packages (from langchain) (6.0.2)\n",
      "Requirement already satisfied: SQLAlchemy<3,>=1.4 in /usr/local/lib/python3.10/dist-packages (from langchain) (2.0.36)\n",
      "Requirement already satisfied: aiohttp<4.0.0,>=3.8.3 in /usr/local/lib/python3.10/dist-packages (from langchain) (3.11.2)\n",
      "Requirement already satisfied: async-timeout<5.0.0,>=4.0.0 in /usr/local/lib/python3.10/dist-packages (from langchain) (4.0.3)\n",
      "Requirement already satisfied: langchain-core<0.4.0,>=0.3.21 in /usr/local/lib/python3.10/dist-packages (from langchain) (0.3.21)\n",
      "Requirement already satisfied: langchain-text-splitters<0.4.0,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from langchain) (0.3.2)\n",
      "Requirement already satisfied: langsmith<0.2.0,>=0.1.17 in /usr/local/lib/python3.10/dist-packages (from langchain) (0.1.143)\n",
      "Requirement already satisfied: numpy<2,>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from langchain) (1.26.4)\n",
      "Requirement already satisfied: pydantic<3.0.0,>=2.7.4 in /usr/local/lib/python3.10/dist-packages (from langchain) (2.9.2)\n",
      "Requirement already satisfied: requests<3,>=2 in /usr/local/lib/python3.10/dist-packages (from langchain) (2.32.3)\n",
      "Requirement already satisfied: tenacity!=8.4.0,<10,>=8.1.0 in /usr/local/lib/python3.10/dist-packages (from langchain) (9.0.0)\n",
      "Requirement already satisfied: openai<2.0.0,>=1.54.0 in /usr/local/lib/python3.10/dist-packages (from langchain-openai) (1.54.4)\n",
      "Requirement already satisfied: tiktoken<1,>=0.7 in /usr/local/lib/python3.10/dist-packages (from langchain-openai) (0.8.0)\n",
      "Collecting SQLAlchemy<3,>=1.4 (from langchain)\n",
      "  Downloading SQLAlchemy-2.0.35-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (9.6 kB)\n",
      "Requirement already satisfied: dataclasses-json<0.7,>=0.5.7 in /usr/local/lib/python3.10/dist-packages (from langchain-community) (0.6.7)\n",
      "Collecting httpx-sse<0.5.0,>=0.4.0 (from langchain-community)\n",
      "  Downloading httpx_sse-0.4.0-py3-none-any.whl.metadata (9.0 kB)\n",
      "Collecting pydantic-settings<3.0.0,>=2.4.0 (from langchain-community)\n",
      "  Downloading pydantic_settings-2.6.1-py3-none-any.whl.metadata (3.5 kB)\n",
      "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (2.4.3)\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.3.1)\n",
      "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (24.2.0)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.5.0)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (6.1.0)\n",
      "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (0.2.0)\n",
      "Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.17.2)\n",
      "Requirement already satisfied: marshmallow<4.0.0,>=3.18.0 in /usr/local/lib/python3.10/dist-packages (from dataclasses-json<0.7,>=0.5.7->langchain-community) (3.23.1)\n",
      "Requirement already satisfied: typing-inspect<1,>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from dataclasses-json<0.7,>=0.5.7->langchain-community) (0.9.0)\n",
      "Requirement already satisfied: jsonpatch<2.0,>=1.33 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.4.0,>=0.3.21->langchain) (1.33)\n",
      "Requirement already satisfied: packaging<25,>=23.2 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.4.0,>=0.3.21->langchain) (24.2)\n",
      "Requirement already satisfied: typing-extensions>=4.7 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.4.0,>=0.3.21->langchain) (4.12.2)\n",
      "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from langsmith<0.2.0,>=0.1.17->langchain) (0.27.2)\n",
      "Requirement already satisfied: orjson<4.0.0,>=3.9.14 in /usr/local/lib/python3.10/dist-packages (from langsmith<0.2.0,>=0.1.17->langchain) (3.10.11)\n",
      "Requirement already satisfied: requests-toolbelt<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from langsmith<0.2.0,>=0.1.17->langchain) (1.0.0)\n",
      "Requirement already satisfied: anyio<5,>=3.5.0 in /usr/local/lib/python3.10/dist-packages (from openai<2.0.0,>=1.54.0->langchain-openai) (3.7.1)\n",
      "Requirement already satisfied: distro<2,>=1.7.0 in /usr/local/lib/python3.10/dist-packages (from openai<2.0.0,>=1.54.0->langchain-openai) (1.9.0)\n",
      "Requirement already satisfied: jiter<1,>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from openai<2.0.0,>=1.54.0->langchain-openai) (0.7.1)\n",
      "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from openai<2.0.0,>=1.54.0->langchain-openai) (1.3.1)\n",
      "Requirement already satisfied: tqdm>4 in /usr/local/lib/python3.10/dist-packages (from openai<2.0.0,>=1.54.0->langchain-openai) (4.66.6)\n",
      "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3.0.0,>=2.7.4->langchain) (0.7.0)\n",
      "Requirement already satisfied: pydantic-core==2.23.4 in /usr/local/lib/python3.10/dist-packages (from pydantic<3.0.0,>=2.7.4->langchain) (2.23.4)\n",
      "Collecting python-dotenv>=0.21.0 (from pydantic-settings<3.0.0,>=2.4.0->langchain-community)\n",
      "  Downloading python_dotenv-1.0.1-py3-none-any.whl.metadata (23 kB)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langchain) (3.4.0)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langchain) (3.10)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langchain) (2.2.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2->langchain) (2024.8.30)\n",
      "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from SQLAlchemy<3,>=1.4->langchain) (3.1.1)\n",
      "Requirement already satisfied: regex>=2022.1.18 in /usr/local/lib/python3.10/dist-packages (from tiktoken<1,>=0.7->langchain-openai) (2024.9.11)\n",
      "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->openai<2.0.0,>=1.54.0->langchain-openai) (1.2.2)\n",
      "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->langsmith<0.2.0,>=0.1.17->langchain) (1.0.7)\n",
      "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->langsmith<0.2.0,>=0.1.17->langchain) (0.14.0)\n",
      "Requirement already satisfied: jsonpointer>=1.9 in /usr/local/lib/python3.10/dist-packages (from jsonpatch<2.0,>=1.33->langchain-core<0.4.0,>=0.3.21->langchain) (3.0.0)\n",
      "Requirement already satisfied: mypy-extensions>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain-community) (1.0.0)\n",
      "Downloading langchain_community-0.3.8-py3-none-any.whl (2.4 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m24.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hDownloading httpx_sse-0.4.0-py3-none-any.whl (7.8 kB)\n",
      "Downloading pydantic_settings-2.6.1-py3-none-any.whl (28 kB)\n",
      "Downloading SQLAlchemy-2.0.35-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m68.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hDownloading python_dotenv-1.0.1-py3-none-any.whl (19 kB)\n",
      "Installing collected packages: SQLAlchemy, python-dotenv, httpx-sse, pydantic-settings, langchain-community\n",
      "  Attempting uninstall: SQLAlchemy\n",
      "    Found existing installation: SQLAlchemy 2.0.36\n",
      "    Uninstalling SQLAlchemy-2.0.36:\n",
      "      Successfully uninstalled SQLAlchemy-2.0.36\n",
      "Successfully installed SQLAlchemy-2.0.35 httpx-sse-0.4.0 langchain-community-0.3.8 pydantic-settings-2.6.1 python-dotenv-1.0.1\n"
     ]
    }
   ],
   "source": [
    "! pip install -U langchain langchain-openai langchain-community"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "66638d6c",
   "metadata": {
    "id": "66638d6c",
    "outputId": "558a51fe-3b59-460d-cc1e-c3d436173000",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/981.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m317.4/981.5 kB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m972.8/981.5 kB\u001b[0m \u001b[31m16.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m981.5/981.5 kB\u001b[0m \u001b[31m13.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.9/62.9 kB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.5/49.5 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m298.0/298.0 kB\u001b[0m \u001b[31m30.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.6/114.6 kB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h  Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
     ]
    }
   ],
   "source": [
    "! pip install -qq tiktoken unstructured pandas lancedb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1cdcac3",
   "metadata": {
    "id": "d1cdcac3"
   },
   "source": [
    "First, let's get some setup out of the way. As we're using the OpenAI API, ensure that you've set your key (and organization if needed):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "58ee1868",
   "metadata": {
    "id": "58ee1868"
   },
   "outputs": [],
   "source": [
    "import openai\n",
    "import os\n",
    "\n",
    "\n",
    "# Configuring the environment variable OPENAI_API_KEY\n",
    "os.environ[\"OPENAI_API_KEY\"] = \"sk-proj-....\"\n",
    "\n",
    "if \"OPENAI_API_KEY\" not in os.environ:\n",
    "    os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"\n",
    "openai.api_key = os.environ[\"OPENAI_API_KEY\"]\n",
    "\n",
    "# assert len(openai.models.list()[\"data\"]) > 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34f524d3",
   "metadata": {
    "id": "34f524d3"
   },
   "source": [
    "\n",
    "We're going to use the power of LangChain to help us create our Q&A bot. It comes with several APIs that can make our development much easier as well as a LanceDB integration for vectorstore."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e11ed32-b742-4012-a37d-af54ea2a2715",
   "metadata": {
    "id": "5e11ed32-b742-4012-a37d-af54ea2a2715"
   },
   "source": [
    "### Importing all libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b55d22f1",
   "metadata": {
    "id": "b55d22f1"
   },
   "outputs": [],
   "source": [
    "import lancedb\n",
    "import re\n",
    "import pickle\n",
    "import requests\n",
    "import zipfile\n",
    "from pathlib import Path\n",
    "\n",
    "from langchain_community.document_loaders import UnstructuredHTMLLoader\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from langchain.vectorstores import LanceDB\n",
    "from langchain_openai import OpenAI, OpenAIEmbeddings\n",
    "from langchain.chains import RetrievalQA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56cc6d50",
   "metadata": {
    "id": "56cc6d50"
   },
   "source": [
    "To make this easier, we've downloaded Pandas documentation and stored the raw HTML files for you to download. We'll download them and then use LangChain's HTML document readers to parse them and store them in LanceDB as a vector store, along with relevant metadata.\n",
    "By default we'll use numpy docs as it is much smaller than pandas docs, but you can replace this with your own docs as well."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f5c79b5-7a43-403d-b456-9276cfe175b5",
   "metadata": {
    "id": "6f5c79b5-7a43-403d-b456-9276cfe175b5"
   },
   "source": [
    "### Get the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7da77e75",
   "metadata": {
    "id": "7da77e75"
   },
   "outputs": [],
   "source": [
    "# pandas_docs = requests.get(\"https://eto-public.s3.us-west-2.amazonaws.com/datasets/pandas_docs/pandas.documentation.zip\")\n",
    "\n",
    "numpy_docs = requests.get(\"https://numpy.org/doc/1.26/numpy-html.zip\")\n",
    "with open(\"numpy-html.zip\", \"wb\") as f:\n",
    "    f.write(numpy_docs.content)\n",
    "\n",
    "file = zipfile.ZipFile(\"numpy-html.zip\")\n",
    "file = file.extractall(path=\"numpy_docs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae42496c",
   "metadata": {
    "id": "ae42496c"
   },
   "source": [
    "We'll create a simple **helper function** that can help to extract metadata, so we can use this downstream when we're wanting to query with filters. In this case, we want to keep the lineage of the uri or path for each document that we process:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d171d062",
   "metadata": {
    "id": "d171d062"
   },
   "outputs": [],
   "source": [
    "# Pre-processing and loading the documentation\n",
    "\n",
    "# Next, let's pre-process and load the documentation. To make sure we don't need to do this repeatedly if we were updating code,\n",
    "# we're caching it using pickle so we can retrieve it again (this could take a few minutes to run the first time you do it).\n",
    "# We'll also add some more metadata to the docs here such as the title and version of the code:\n",
    "import re\n",
    "\n",
    "\n",
    "def get_document_title(document_list):\n",
    "    titles = []\n",
    "    for doc in document_list:\n",
    "        if \"metadata\" in doc and \"source\" in doc[\"metadata\"]:\n",
    "            m = str(doc[\"metadata\"][\"source\"])\n",
    "            title = re.findall(\"numpy_docs(.*).html\", m)\n",
    "            print(title)\n",
    "            if title:\n",
    "                titles.append(title[0])\n",
    "            else:\n",
    "                titles.append(\"\")\n",
    "        else:\n",
    "            titles.append(\"\")\n",
    "    return titles"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2abacdc6-6f40-4d96-99bb-0e4ef18ffdfa",
   "metadata": {
    "id": "2abacdc6-6f40-4d96-99bb-0e4ef18ffdfa"
   },
   "source": [
    "# Pre-processing and loading the documentation\n",
    "\n",
    "Next, let's pre-process and load the documentation. To make sure we don't need to do this repeatedly if we were updating code, we're caching it using pickle so we can retrieve it again (this could take a few minutes to run the first time you do it). We'll also add some more metadata to the docs here such as the title and version of the code:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5609932d-21fd-4b39-a3e9-12b709358751",
   "metadata": {
    "id": "5609932d-21fd-4b39-a3e9-12b709358751"
   },
   "source": [
    "If there is some issue with nltk package, kindly try using\n",
    "```\n",
    "import nltk\n",
    "nltk.download('punkt')\n",
    "```\n",
    "or try to manually install the [nltk_data](https://github.com/nltk/nltk_data/tree/gh-pages) package and unzip the **punkt tokenizer** zip and the **averaged_perceptron_tagger** zip file in the packages folder."
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import nltk\n",
    "\n",
    "nltk.download(\"punkt\")"
   ],
   "metadata": {
    "id": "CMIplDGNSCIq",
    "outputId": "c1971940-748b-44b2-9a89-ce9bcc6f8604",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "id": "CMIplDGNSCIq",
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
      "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ]
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      "numpy_docs/reference/c-api/coremath.html\n",
      "numpy_docs/reference/c-api/generalized-ufuncs.html\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "\r2691it [08:17,  6.66it/s]"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "numpy_docs/reference/c-api/config.html\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "\r2692it [08:17,  5.22it/s]"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "numpy_docs/reference/c-api/dtype.html\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "\r2693it [08:18,  3.05it/s]"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "numpy_docs/reference/c-api/iterator.html\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "\r2694it [08:19,  2.84it/s]"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "numpy_docs/reference/c-api/ufunc.html\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "\r2695it [08:19,  3.12it/s]"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "numpy_docs/reference/c-api/data_memory.html\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "\r2696it [08:20,  2.42it/s]"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "numpy_docs/reference/c-api/array.html\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "2699it [08:21,  5.38it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "numpy_docs/reference/distutils/misc_util.html\n",
      "numpy_docs/_static/webpack-macros.html\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "docs = []\n",
    "docs_path = Path(\"docs.pkl\")\n",
    "for p in tqdm(Path(\"numpy_docs\").rglob(\"*.html\")):\n",
    "    if p.is_dir():\n",
    "        continue\n",
    "    print(p)\n",
    "    loader = UnstructuredHTMLLoader(str(p))\n",
    "    raw_document = loader.load()\n",
    "    # docs.append(raw_document)\n",
    "    title = get_document_title(raw_document)\n",
    "    m = {\"title\": title}\n",
    "    if raw_document:\n",
    "        raw_document[0].metadata.update(m)\n",
    "        raw_document[0].metadata[\"source\"] = str(raw_document[0].metadata[\"source\"])\n",
    "        docs.extend(raw_document)\n",
    "\n",
    "\n",
    "if docs:\n",
    "    with open(docs_path, \"wb\") as fh:\n",
    "        pickle.dump(docs, fh)\n",
    "else:\n",
    "    with open(docs_path, \"rb\") as fh:\n",
    "        docs = pickle.load(fh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c019d728-bb65-494a-b4a9-73a62bf8e155",
   "metadata": {
    "id": "c019d728-bb65-494a-b4a9-73a62bf8e155",
    "outputId": "a63ba879-bac3-4017-818f-f1ad83e11f41",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "2699"
      ]
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "len(docs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3852dd3",
   "metadata": {
    "id": "c3852dd3"
   },
   "source": [
    "# Generating emebeddings from our docs\n",
    "\n",
    "Now that we have our raw documents loaded, we need to pre-process them to generate embeddings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "82230563",
   "metadata": {
    "id": "82230563"
   },
   "outputs": [],
   "source": [
    "text_splitter = RecursiveCharacterTextSplitter(\n",
    "    chunk_size=1000,\n",
    "    chunk_overlap=200,\n",
    ")\n",
    "documents = text_splitter.split_documents(docs)\n",
    "embeddings = OpenAIEmbeddings()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43e68215",
   "metadata": {
    "id": "43e68215"
   },
   "source": [
    "# Storing\n",
    "\n",
    "Let's connect to LanceDB so we can store our documents, It requires 0 setup !"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ed0c792",
   "metadata": {
    "id": "7ed0c792"
   },
   "source": [
    "### For LanceDB cloud use the below cell :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74780a58",
   "metadata": {
    "id": "74780a58"
   },
   "outputs": [],
   "source": [
    "vectorstore = LanceDB(\n",
    "    embedding=embeddings,\n",
    "    uri=\"db://test\",  # your remote database URI\n",
    "    api_key=\"sk_...\",\n",
    "    region=\"us-east-x-xxx\",  # the cloud region you have configured\n",
    "    table_name=\"langchain_vectorstore\",  # Optional, defaults to \"vectors\"\n",
    "    mode=\"overwrite\",  # Optional, defaults to \"overwrite\"\n",
    ")\n",
    "\n",
    "doc_ids = vectorstore.add_documents(documents=documents)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d6cf585",
   "metadata": {
    "id": "9d6cf585"
   },
   "source": [
    "### For LanceDB local use the below cell :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "71e15e3b",
   "metadata": {
    "id": "71e15e3b"
   },
   "outputs": [],
   "source": [
    "vectorstore = LanceDB.from_documents(documents=documents, embedding=embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cb1dc5d",
   "metadata": {
    "id": "3cb1dc5d"
   },
   "source": [
    "Now let's create our RetrievalQA chain using the LanceDB vector store:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6a5891ad",
   "metadata": {
    "id": "6a5891ad"
   },
   "outputs": [],
   "source": [
    "qa = RetrievalQA.from_chain_type(\n",
    "    llm=OpenAI(), chain_type=\"stuff\", retriever=vectorstore.as_retriever()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28d93b85",
   "metadata": {
    "id": "28d93b85"
   },
   "source": [
    "And thats it! We're all setup. The next step is to run some queries, let's try a few:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82a5c946-5af3-45c1-862a-207ff4a34bb4",
   "metadata": {
    "id": "82a5c946-5af3-45c1-862a-207ff4a34bb4"
   },
   "source": [
    "### Querying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "70d88316",
   "metadata": {
    "id": "70d88316",
    "outputId": "da5c4c94-819f-489b-a546-87f5f2deb0b7",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'query': 'tell me about the numpy library?',\n",
       " 'result': ' The NumPy library is a Python library that provides multidimensional array and matrix data structures, as well as various routines for efficient operations on these structures. It is commonly used for scientific computing and has a vast library of mathematical functions. It also has a fixed array size and is used extensively in other popular data science packages such as Pandas and scikit-learn.'}"
      ]
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "source": [
    "query = \"tell me about the numpy library?\"\n",
    "qa.invoke(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "85a0397c",
   "metadata": {
    "id": "85a0397c",
    "outputId": "0ed3ddb5-9e46-4c6c-e830-252d6933c9f0",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'query': \"What's the current version of numpy?\",\n",
       " 'result': ' The current version of NumPy is 1.26.'}"
      ]
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "source": [
    "query = \"What's the current version of numpy?\"\n",
    "qa.invoke(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "923f86c6",
   "metadata": {
    "id": "923f86c6",
    "outputId": "1e0fee97-df4e-4e0b-80f9-8715e489f873",
    "colab": {
     "base_uri": "https://localhost:8080/"
    }
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'query': 'What kind of linear algebra related operations can be done in numpy?',\n",
       " 'result': \"\\n\\nSome possible operations that can be performed using numpy's linear algebra functions include matrix multiplication, finding eigenvalues and eigenvectors, calculating determinants and inverses, solving systems of linear equations, and performing decompositions such as LU, QR, and SVD.\"}"
      ]
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "source": [
    "query = \"What kind of linear algebra related operations can be done in numpy?\"\n",
    "qa.invoke(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8958d1b-0ad6-44d6-bca0-d81771c564a1",
   "metadata": {
    "id": "f8958d1b-0ad6-44d6-bca0-d81771c564a1"
   },
   "source": [
    "Thanks !"
   ]
  }
 ],
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